print(Sys.Date())
## [1] "2023-01-07"
a <- list(10, TRUE, 5.6)

ls(pat = "^V")
## character(0)

1 Objective

This notebook generate the result of CO2 data analysis. Data set contains a collection of soil characteristics, measured co2 emission collected from incubation study. Soil samples was collected from two cranberry fied stand of eastern Canada. Incubation study was carried out at Agriculture and Agri-food Canada(Sainte-foy, Quebec,qc) from February to Mai 2019. The aim of this study was to measure CO2 emission rates in cranberry soils of Eastern Canada as related to soil temperature and depth

2 Statistical questions

In addition to data exploration, this notebook will answer the following statistical questions.

  1. What is the influence of soil depth and temperature on CO2 emission?
  2. Can Arrhenius equation and Q10 be useful to describe temperature sensitivity of carbon decomposition across layers?

3 Packages

We need package tidyverse which loads a set of packages for easy data manipulation(Ex: dplyr) and visualization (ex: ggplot2). We also use ggpubr to customise publication ready plot, ggpmisc and grid are useful packages as extensions to ggplot2.

4 Import data

We load two data data_pot and data_co2 involved in our anylisis. data_pot contained details about sites sampling, soil sampling(soil depth, weight, water content and bulk density), laboratory incubation temperature while data_co2 contained details about laboratory incubation time, co2 emission and jar masson details. data_co2 was combined with data_pot with left_join function

## # A tibble: 72 × 12
##    ID po…¹ Sites Depth…² Repet…³ Tempe…⁴ Pot w…⁵ Soil …⁶ Water…⁷ Water…⁸ Bulk …⁹
##      <dbl> <chr>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
##  1       6 A9         10       1      10    245.    110.    42.1    10.3    0.89
##  2      21 A9         10       1      20    251.    110.    42.1    10.3    0.89
##  3      54 A9         10       1      30    250.    110.    42.1    10.3    0.89
##  4      18 A9         10       2      10    246.    125.    27.5    24.9    0.89
##  5      59 A9         10       2      20    248.    125.    27.5    24.9    0.89
##  6      60 A9         10       2      30    255.    125.    27.5    24.9    0.89
##  7      41 A9         10       3      20    248.    117.    35.5    16.9    0.89
##  8      55 A9         10       3      10    249.    117.    35.5    16.9    0.89
##  9      61 A9         10       3      30    249.    117.    35.5    16.9    0.89
## 10      20 A9         10       4      10    245.    123.    28.7    23.7    0.89
## # … with 62 more rows, 2 more variables: `Carbone(%)` <dbl>, pHCaCl2 <dbl>, and
## #   abbreviated variable names ¹​`ID pot`, ²​`Depth (cm)`, ³​Repetition,
## #   ⁴​`Temperature (°C)`, ⁵​`Pot weight (g)`, ⁶​`Soil weight (g)`,
## #   ⁷​`Water volume (ml)`, ⁸​`Water content (%)`, ⁹​`Bulk density (g/cm3)`

5 Some calculations

Several variables have been added to our data in order to proceed for analysis. The added variables are the following: Temperature (Kelvin), Molar Volume (L/mol), Headspace Volume (mL), Dry soil weight (g), CO2 emission (ug/h/g), CO2 emission (mg/kg), decomposition rate K, lnKand 1/T(T = Temperature(Kelvin)

6 Exploratory data analysis

6.1 Histogram

New.labs <- c("10°C", "20°C", "30°C") # Change labels 
names(New.labs) <- c("10", "20", "30")

New.labs_b <-  c("[0-10 cm]", "[10-20 cm]", "[20-30 cm]") # Change labels
names(New.labs_b) <- c("10", "20", "30")
library(plotly)
ggplotly(  
  data_co2 |>
    ggplot() +
    geom_histogram(aes(x = log10(`CO2 emission (ug/h/g)`)), bins = 150)
)
## Warning in FUN(X[[i]], ...): NaNs produced

## Warning in FUN(X[[i]], ...): NaNs produced
## Warning: Removed 37 rows containing non-finite values (`stat_bin()`).

Data contains some outliers, let remove them

data_co2_clean <- data_co2 |> 
  mutate(log_tr = log10(`CO2 emission (ug/h/g)`)) |>
  filter(log_tr > -3.06 & log_tr < -0.33) |>
  drop_na()
## Warning in mask$eval_all_mutate(quo): NaNs produced

Now data look well distributed

ggplotly(  
  data_co2_clean |>
    ggplot() +
    geom_histogram(aes(x = log10(`CO2 emission (ug/h/g)`)), bins = 100) 
)

6.2 Correlations

data_co2_clean |>
  select(`Time (days)`, `Depth (cm)`, `Temperature (°C)`, 
         `CO2 emission (ug/h/g)`) |>
  corrr::correlate() |>
  corrr::focus(`CO2 emission (ug/h/g)`) |>
  mutate(term = fct_reorder(term, `CO2 emission (ug/h/g)`)) |>
  ggplot(aes(x = `CO2 emission (ug/h/g)`, y= term)) +
  geom_col(width = 0.2) +
  labs(x = bquote(~CO[2]~ 'emision ('*mu~'g'~ h^-1~g^-1*')')) +
  theme_bw()
## Correlation computed with
## • Method: 'pearson'
## • Missing treated using: 'pairwise.complete.obs'

6.3 Boxplot

options(repr.plot.width = 6, repr.plot.height = 7)
pg <- ggplot(data=data_co2_clean, aes(x = `Time (days)`,
                                      y = `CO2 emission (ug/h/g)` )) +
  geom_boxplot(aes(group = factor(`Time (days)`))) + 
  facet_grid(`Depth (cm)` ~ `Temperature (°C)`,   scales = "free", 
             labeller = labeller(`Depth (cm)` = New.labs_b, 
                                 `Temperature (°C)` = New.labs))+ 
  labs(x = "Incubation time (days)", y = bquote(~CO[2]~ 
                                    'emision ('*mu~'g'~ h^-1~g^-1*')'))
pg 

ggsave("figures/Boxplot.png", width = 6, height = 7, dpi = 600)

7 What is the influence of soil depth and temperature on CO2 emission?

7.1 Build model: linear regression

model_rec <-  data_co2_clean |>
  recipe(`CO2 emission (ug/h/g)` ~ ., data_co2) |> 
  step_select(`CO2 emission (ug/h/g)`, `Time (days)`, Sites, 
              `Depth (cm)`, `Temperature (°C)`) |>
  step_log(all_outcomes(), base = 10) |>
  step_dummy(Sites) |>
  step_normalize(all_numeric(), -all_outcomes() ) |>
  prep()

data_co2_preprocessed <-  juice(model_rec)
model_spec <- linear_reg() |>
  set_engine("lm")

7.1.1 Fit model

model_fit <- model_spec |>
  fit(`CO2 emission (ug/h/g)` ~ ., data_co2_preprocessed)

7.1.2 Exploring model results

tidy(model_fit)
## # A tibble: 5 × 5
##   term               estimate std.error statistic   p.value
##   <chr>                 <dbl>     <dbl>     <dbl>     <dbl>
## 1 (Intercept)         -1.70     0.00983   -173.   0        
## 2 `Time (days)`       -0.103    0.00985    -10.5  1.22e- 23
## 3 `Depth (cm)`        -0.579    0.00993    -58.3  3.08e-247
## 4 `Temperature (°C)`   0.273    0.00992     27.6  3.81e-108
## 5 Sites_PF45          -0.0202   0.00984     -2.05 4.11e-  2
glance(model_fit)
## # A tibble: 1 × 12
##   r.squared adj.r.squ…¹ sigma stati…²   p.value    df logLik   AIC   BIC devia…³
##       <dbl>       <dbl> <dbl>   <dbl>     <dbl> <dbl>  <dbl> <dbl> <dbl>   <dbl>
## 1     0.868       0.867 0.240    969. 6.71e-258     4   7.36 -2.71  23.6    34.0
## # … with 2 more variables: df.residual <int>, nobs <int>, and abbreviated
## #   variable names ¹​adj.r.squared, ²​statistic, ³​deviance

7.1.3 Inspect the model

7.1.4 Prediction

prediction <-  model_fit |>
  predict(data_co2_preprocessed)

7.1.5 collect Metrics

rmse <-  data_co2_preprocessed |>
  bind_cols(prediction) |>
  rmse(`CO2 emission (ug/h/g)`, .pred)
rmse
## # A tibble: 1 × 3
##   .metric .estimator .estimate
##   <chr>   <chr>          <dbl>
## 1 rmse    standard       0.239
rmse <-  round(as.numeric(rmse[1,3]), 2)
rsq <-  data_co2_preprocessed |>
  bind_cols(prediction) |>
  rsq(`CO2 emission (ug/h/g)`, .pred)
rsq
## # A tibble: 1 × 3
##   .metric .estimator .estimate
##   <chr>   <chr>          <dbl>
## 1 rsq     standard       0.868
rsq <- round(as.numeric(rsq[1,3]), 2)
options(repr.plot.width = 4, repr.plot.height = 2)
px <- data_co2_preprocessed |>
  bind_cols(prediction) |>
  ggplot(aes(x = `CO2 emission (ug/h/g)`, y = .pred)) +
  geom_point(size = 1, alpha = .3) +
  geom_label(aes(x = -3, y = -.6),
             vjust = 1, hjust = 0, size = 2, label.size = 0.1,
             label = paste("R² =",  rsq, "\nRMSE =", rmse)) +
  geom_abline() +
  scale_x_continuous(breaks = c(-3, -2, -1), labels = c("–3", "–2", "–1")) +
  scale_y_continuous(breaks = c(-3, -2, -1), labels = c("–3", "–2", "–1")) +
  theme_pubr()  +
  theme(axis.title=element_text(size=7),
        axis.line = element_line(size = 0.1),
        axis.ticks = element_line(size = 0.1),
        axis.text = element_text(size = 6)) +
  labs(x= bquote("Observed log"~CO[2]~ 
                   'emission rate ('*mu~'g'~ h^-1~g^-1*')'),
       y = bquote("Predicted log"~CO[2]~ 
                    'emission rate ('*mu~'g'~ h^-1~g^-1*')'))
px  

ggsave("figures/Observed and predicted co2 emission.png", width = 4, 
       height = 2.5, dpi = 600)

7.2 Variable coefficient and confidence intervals

options(repr.plot.width = 8, repr.plot.height = 2)
term_rename <- tibble(term = c("`Time (days)`", "`Depth (cm)`", 
                               "`Temperature (°C)`", "Sites_PF45"),
                      name_corrected = c("Elapsed time", "Layer", "Temperature", "Management"))

h <- broom::tidy(model_fit, conf.int = TRUE) |>
  dplyr::filter(term != "(Intercept)") |>
  left_join(term_rename, by = "term") |>
  mutate(term_rename = fct_reorder(name_corrected, estimate)) |>
  ggplot(aes(estimate, term_rename)) +
  geom_vline(xintercept = 0, linetype = 2) +
  geom_point() +
  geom_errorbarh(aes(xmin = conf.low, xmax = conf.high), height = 0.1,
                 size=0.5) +
  scale_x_continuous(breaks = c(-0.6, -0.4, -0.2, 0.0, 0.2), 
                     labels = c("–0.6", "–0.4", "–0.2", "0.0", "0.2")) +
  labs(x = "Coefficient", y = "") +
  theme_light() +
  theme(axis.text=element_text(size=14),
        axis.title=element_text(size=14)) # Time (d)
h

ggsave("figures/Linear-model-Co2_with_site.png", width = 8, 
       height = 2, dpi = 600)

7.3 Prediction model of CO2 emission in cranberry soils in three-layer positions (0-10 cm, 10-20 cm, 20-30 cm) and at three temperatures (10, 20 and 30oC).

New.labs <- c("10°C", "20°C", "30°C") # Change labels 
names(New.labs) <- c("10", "20", "30")

New.labs_b <-  c("[0–10 cm]", "[10–20 cm]", "[20–30 cm]") # Change labels
names(New.labs_b) <- c("10", "20", "30")

options(repr.plot.width = 8, repr.plot.height = 6)
pl <- data_co2_clean |>
  bind_cols(10^prediction) |>
  ggplot(aes(x = `Time (days)`, y = `CO2 emission (ug/h/g)`)) +
  geom_smooth(aes(x = `Time (days)`, y = `.pred`), color = "black", size = .5) +
  geom_point(size = 2, alpha = .3) +
  facet_grid(`Depth (cm)` ~ `Temperature (°C)`, scales = "free", 
             labeller = labeller(`Depth (cm)` = New.labs_b, 
                                 `Temperature (°C)` = New.labs)) +
  scale_y_log10() +
  theme_bw() +
  theme(strip.text = element_text(size = 12), axis.text=element_text(size=12),
        axis.title=element_text(size=14),
        axis.title.y = element_text(size=14)) +
  xlab("Incubation time (d)") + ylab(bquote(~CO[2]~ 'emission rate ('*mu~'g'~ h^-1~g^-1*')'))
pl

ggsave("figures/CO2 emission.png", plot= pl, width = 7, height = 5, dpi = 600)

8 What is the temperature sensitivity across cranberry soil layers?

8.1 Fit of Arrhenius equation

The Arrhenius equation has been used to describe temperature sensitivity to CO2 emission. The Arrhenius equation was computed as follows:

\[k = A e^{{\frac{-Ea}{RT}}}\]

\[log \left( k \right) = log \left( A e^{\frac{-Ea}{RT}} \right)\]

\[log \left( k \right) = log \left( A \right) + log \left(e^{\frac{-Ea}{RT}} \right)\]

\[log \left( k \right) = log \left( A \right) - \frac{1}{T} \times \left(\frac{Ea}{R}\right)\]

Where \(A\) is the pre-exponential factor and \(Ea\) is activation energy assumed to be independent of temperature, \(R\) is the universal gas constant and \(T\) is absolute temperature (Kelvin)

models_co2 <- data_co2 %>%
  group_by(`Depth (cm)`) %>%
  summarise(linmod = list(lm(lnK ~ `1/T`)))
models_co2
## # A tibble: 3 × 2
##   `Depth (cm)` linmod
##          <dbl> <list>
## 1           10 <lm>  
## 2           20 <lm>  
## 3           30 <lm>
linmod_coef <- list()
for (i in seq_along(models_co2$linmod)) linmod_coef[[i]] <- models_co2$linmod[[i]]$coefficients
linmod_coef <- do.call(rbind.data.frame, linmod_coef)
names(linmod_coef) <- c("Intercept", "Slope")
linmod_coef <- bind_cols(unique(data_co2["Depth (cm)"]), linmod_coef)
linmod_coef
## # A tibble: 3 × 3
##   `Depth (cm)` Intercept  Slope
##          <dbl>     <dbl>  <dbl>
## 1           10      11.6 -6002.
## 2           20      14.0 -7052.
## 3           30      18.5 -8558.
options(repr.plot.width = 12, repr.plot.height = 6)
plot_co2 <- data_co2_clean %>%
  ggplot(aes(x = `1/T`, y = lnK)) +
  facet_grid(~`Depth (cm)`, labeller = labeller(`Depth (cm)` = New.labs_b)) +
  geom_boxplot(aes(group = factor(`1/T`)), outlier.shape = NA) +
  stat_regline_equation(aes(label =  paste(..eq.label.., ..rr.label.., 
                                           sep = "~~")), label.x = 0.00331, 
                        label.y = -7) +
  geom_abline(data = linmod_coef, aes(intercept = Intercept, slope = Slope), 
              lwd = 1) +
  scale_y_continuous(breaks = c(-12, -10, -8), labels = c("–12", "–10", "–8")) +
  labs(x =  bquote("1/T" ~(ᵒK^-1)), y = bquote("Log k" ~(d^-1))) +
  theme_bw() +
  theme(strip.text = element_text(size = 14), axis.text=element_text(size=14),
        axis.title=element_text(size=14)) 
plot_co2

ggsave("figures/Arrhénius équation.png", plot = plot_co2, width = 8, 
       height = 4, dpi = 600)# export plot high resolution

8.2 Activation Energy computation

Activation_energy <- tibble(
  Soil_layers = c("10", "20", "30"),
  intercept = NA,
  slope = NA,
  adj_r_sq = NA
)
lm_arrhenius <- for (i in 1:nrow(Activation_energy)) {
  
  lm_Activation_energy <- data_co2_clean %>%
    filter(`Depth (cm)` == Activation_energy$Soil_layers[i]) %>%
    lm(lnK ~ `1/T`, data = .)
  
  # intercept
  Activation_energy$intercept[i] <- coef(lm_Activation_energy)[1]
  
  # Slope
  Activation_energy$slope[i] <- coef(lm_Activation_energy)[2]
  
  # statistics
  Activation_energy$adj_r_sq[i] <- summary(lm_Activation_energy)$adj.r.squared
}
R = 8.3144621 / 1000 # Gas constant Kj/mol/K 
Activation_energy <-  Activation_energy %>%
  mutate(Ea = -slope * R) %>%
  select(Soil_layers, adj_r_sq, Ea)
Activation_energy
## # A tibble: 3 × 3
##   Soil_layers adj_r_sq    Ea
##   <chr>          <dbl> <dbl>
## 1 10             0.477  47.5
## 2 20             0.402  58.6
## 3 30             0.507  64.9

8.3 Computing K median in order to compute Q10 value accross soil depth

K_median <- aggregate(K ~ `Sites` + `Time (days)` + `Depth (cm)` + 
                        `Temperature (°C)`, data = data_co2_clean, FUN = median)

K_median <- K_median %>%
  pivot_wider(names_from = `Temperature (°C)`, values_from = K)


K_median$Q_20_10 <- K_median$`20` / K_median$`10`
K_median$Q_30_20 <- K_median$`30` / K_median$`20`

K_median <- K_median %>%
  na.omit(K_median)
data_Q10 <- gather(data = K_median, key = `Temperature range`, 
                   value = Q10, c(`Q_20_10`, `Q_30_20`),
                   factor_key=TRUE)

stat_Q10 <- data_Q10 |>
  group_by(`Depth (cm)`) |>
  get_summary_stats(Q10)
stat_Q10
## # A tibble: 3 × 14
##   Depth (…¹ varia…²     n   min   max median    q1    q3   iqr   mad  mean    sd
##       <dbl> <fct>   <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1        10 Q10        36 0.785  3.53   1.82  1.67  2.32 0.647 0.542  1.99 0.568
## 2        20 Q10        36 1.08   5.87   2.05  1.79  2.63 0.845 0.579  2.33 0.936
## 3        30 Q10        24 0.627  4.94   2.63  2.17  3.05 0.879 0.709  2.64 0.873
## # … with 2 more variables: se <dbl>, ci <dbl>, and abbreviated variable names
## #   ¹​`Depth (cm)`, ²​variable
New.labs_c <- c("[10°C–20°C]", "[20°C–30°C]") # Change labels 
names(New.labs_c) <- c("Q_20_10", "Q_30_20")

options(repr.plot.width = 8, repr.plot.height = 4)
 data_Q10 |> 
  mutate(`Layers` = as.character(`Depth (cm)`)) |>
  ggplot(aes(x = `Time (days)`, y = `Q10`)) +
  facet_grid(`Temperature range`~`Depth (cm)`, 
             labeller = labeller(`Depth (cm)` = New.labs_b,
                                 `Temperature range` = New.labs_c)) +
  geom_smooth(method = "lm", se = TRUE, color = "Black") +
  geom_point(size = 1.5, alpha = 0.6) +
  labs(x = "Time(d)", y = bquote(Q[10])) +
  theme_bw() + 
  theme(strip.text = element_text(size = 14), axis.text=element_text(size=14),
        axis.title=element_text(size=14)) 

ggsave("figures/Variation of Q10 across layers.png", width = 8, 
       height = 4, dpi = 600)# export plot high resolution

9 Soil description

9.1 Soil layers properties

Import data

data_carbon_credit <- read_csv2('data/data_carbon_credit.csv')
## ℹ Using "','" as decimal and "'.'" as grouping mark. Use `read_delim()` for more control.
## Rows: 24 Columns: 15
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ";"
## chr  (3): Location, Layer (cm), 0_30_ID
## dbl (12): Sample, Site age, Repetition, Bulk density (kg m-3), pHCaCl2, Sand...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
data_carbon_credit <- data_carbon_credit |>
  mutate(`C:N ratio` = `Carbone (%)` / `Nitrogen (%)`)

Some calculations

mean_sd_CoverN <- data_carbon_credit |>
  group_by(`Layer (cm)`) |>
  summarize(mean_C_over_N = mean(`C:N ratio`, na.rm = TRUE), 
                        se_C_over_N = sd(`C:N ratio`, na.rm = TRUE)/
              sqrt(length(!is.na(`C:N ratio`))))
mean_sd_CoverN
## # A tibble: 3 × 3
##   `Layer (cm)` mean_C_over_N se_C_over_N
##   <chr>                <dbl>       <dbl>
## 1 [0-10]               20.1         1.05
## 2 [10-20]              16.0         1.91
## 3 [20-30]               9.02        1.96
data_carbon_credit |> get_summary_stats(`C stock (kg m-3)`)
## # A tibble: 1 × 13
##   variable        n   min   max median    q1    q3   iqr   mad  mean    sd    se
##   <fct>       <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 C stock (k…    24  1.67  30.9   12.5  6.57  16.7  10.1  7.78  12.1  7.05  1.44
## # … with 1 more variable: ci <dbl>
data_carbon_credit |> 
  group_by(`Layer (cm)`) |>
get_summary_stats(`C stock (kg m-3)`)
## # A tibble: 3 × 14
##   Layer (…¹ varia…²     n   min   max median    q1    q3   iqr   mad  mean    sd
##   <chr>     <fct>   <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 [0-10]    C stoc…     8 11.4   22.2  16.2  15.1  17.4   2.35  1.60 16.6   3.26
## 2 [10-20]   C stoc…     8  6.52  30.9  11.8   6.74 17.1  10.4   7.62 13.6   8.35
## 3 [20-30]   C stoc…     8  1.67  14.8   5.36  3.31  7.42  4.11  3.04  6.09  4.06
## # … with 2 more variables: se <dbl>, ci <dbl>, and abbreviated variable names
## #   ¹​`Layer (cm)`, ²​variable
data_carbon_credit |> 
  group_by(`Layer (cm)`) |>
get_summary_stats(`C:N ratio`)
## # A tibble: 3 × 14
##   Layer (…¹ varia…²     n   min   max median    q1    q3   iqr   mad  mean    sd
##   <chr>     <fct>   <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 [0-10]    C:N ra…     8  15    23.3  20.9  17.8   22.5  4.72  2.98 20.1   2.97
## 2 [10-20]   C:N ra…     8  10    24.4  15.8  11.5   20    8.5   6.18 16.0   5.40
## 3 [20-30]   C:N ra…     8   2.5  20     8.33  5.96  10.6  4.66  4.32  9.02  5.54
## # … with 2 more variables: se <dbl>, ci <dbl>, and abbreviated variable names
## #   ¹​`Layer (cm)`, ²​variable
library(viridis)
plot_desc <- function(y, ylab){
  New.labs_c <-  c("Site A9", "Site 45") # Change labels
names(New.labs_c) <- c("Belanger/ A9", "Fortier/ 45")
  ggplot(data_carbon_credit, aes(`Layer (cm)`, y, color = `Layer (cm)`)) +
    geom_boxplot(outlier.shape = NA) + 
facet_grid( . ~ `Location`, scales = "free", 
            labeller = labeller(`Location` = New.labs_c)) +
    theme_bw() +
    scale_color_viridis_d(option = "G", begin = .1, end = .8) +
    scale_x_discrete(labels = c("[0–10 cm]", "[10–20 cm]", "[20–30 cm]")) +
theme(strip.text = element_text(size = 11), axis.text=element_text(size=11),
      axis.text.x = element_text(size = 8),
        axis.title=element_text(size=11), legend.position = "none") +
    
    labs(y = ylab)
  }

plot1 <- plot_desc(data_carbon_credit$`C stock (kg m-3)`, 
                   bquote("C stock (kg" ~m^-3~")"))
plot2 <- plot_desc(data_carbon_credit$`C:N ratio`, "C:N ratio")
plot3 <- plot_desc(data_carbon_credit$`Bulk density (kg m-3)`, # m-3
                   "Bulk density (kg"~m^-3~")")
plot4 <- plot_desc(data_carbon_credit$pHCaCl2, bquote(pHCaCl[2]))
plot5 <- plot_desc(data_carbon_credit$`Total porosity`, "Total porosity (%)")
plot6 <- plot_desc(data_carbon_credit$`Water content (%)`, "Water content (%)")

options(repr.plot.width = 8, repr.plot.height = 6)
figure <- ggarrange(plot1, plot2, plot3, plot4, plot5, plot6,
                    labels = c("A", "B", "C", "D", "E", "F"), label.x = 0.05, 
                    label.y = 1.01,
                    ncol = 2, nrow = 3)
figure

ggsave("figures/Soil description.png", width = 9, height = 6, dpi = 600)
# export plot high resolution

10 C:N ratio in alternate sublayers of sand and organic matter

Data loading

Carbon_credit <- read_csv2('data/data_carbon_sublayer.csv')
## ℹ Using "','" as decimal and "'.'" as grouping mark. Use `read_delim()` for more control.
## Rows: 23 Columns: 20
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ";"
## chr  (6): Projet, Site, Horizon, Layers, Soil texture, Munsell_color
## dbl (14): Depht (cm), Thickness(cm), Bulk density(kg m-3), Site_age, Weigh_s...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Carbon_credit
## # A tibble: 23 × 20
##    Projet   Site  Horizon Depht…¹ Thick…² Layers Bulk …³ Soil …⁴ Site_…⁵ Munse…⁶
##    <chr>    <chr> <chr>     <dbl>   <dbl> <chr>    <dbl> <chr>     <dbl> <chr>  
##  1 Pedology Bela… H1          1.8     1.8 [0-1.…    913. Sand         14 10YR -…
##  2 Pedology Bela… H2          2.2     0.4 [1.8-…    913. Organi…      14 10YR -…
##  3 Pedology Bela… H3          3.2     1   [2.2-…    913. Sand         14 2,5Y -…
##  4 Pedology Bela… H4          3.6     0.4 [3.2-…    913. Organi…      14 10YR -…
##  5 Pedology Bela… H5          5.1     1.5 [3.6-…    913. Sand         14 10YR -…
##  6 Pedology Bela… H6          5.8     0.7 [5.1-…    913. Organi…      14 10YR -…
##  7 Pedology Bela… H7          9.5     3.7 [5.8-…    913. Sand         14 2,5Y -…
##  8 Pedology Bela… H8         12       2   [9.5-…   1384. Organi…      14 10YR -…
##  9 Pedology Bela… H9         12.5     0.5 [12-1…   1384. Sand         14 10YR -…
## 10 Pedology Bela… H10        19.2     6.7 [12.5…   1384. Sand         14 10YR -…
## # … with 13 more rows, 10 more variables: Weigh_superior_2MM <dbl>,
## #   `Weigh _0_2MM` <dbl>, Repetition <dbl>, pHCaCl2 <dbl>, CTRL_C_pourc <dbl>,
## #   CTRL_S_pourc <dbl>, CTRL_N_pourc <dbl>, C_pourc <dbl>, S_pourc <dbl>,
## #   N_pourc <dbl>, and abbreviated variable names ¹​`Depht (cm)`,
## #   ²​`Thickness(cm)`, ³​`Bulk density(kg m-3)`, ⁴​`Soil texture`, ⁵​Site_age,
## #   ⁶​Munsell_color

C:N ratio computation

Carbon_credit <- Carbon_credit %>%
mutate(`C/N` = C_pourc/N_pourc)

Generating the plots

options(repr.plot.width=8, repr.plot.height=4)
New.labs_d <-  c("Site A9", "Site 45") # Change labels
names(New.labs_d) <- c("Belanger/A9", "Fortier/45")

ggplot(data=Carbon_credit, aes(x= `Depht (cm)`, y= `C/N`)) +
  facet_grid(.~Site, labeller = labeller(`Site` = New.labs_d)) +
  geom_line(linetype = "twodash") +
  geom_point(aes(shape = `Soil texture`, fill = `Soil texture`), size = 3) +
  scale_shape_manual(values=c(21, 21))+
  scale_fill_manual(values = c("#000000", "#FFFFFF")) +
  scale_y_continuous(breaks = 5*0:1000,
                     expand = expand_scale(add = 5)) +
  scale_x_continuous(breaks = 5*0:1000,
                     expand = expand_scale(add = 5)) +
  theme(strip.text = element_text(face = "bold"), 
        axis.text=element_text(face = "bold"),
        axis.title=element_text(face = "bold") , 
        legend.title= element_text(face = "bold"),
        legend.text = element_text(face = "bold")) +
  labs(y= "C/N Ratio") +
  coord_flip()
## Warning: `expand_scale()` was deprecated in ggplot2 3.3.0.
## ℹ Please use `expansion()` instead.

ggsave("figures/(C_over_N).png", width = 8, height = 4, dpi = 800)